erikw

#!/usr/bin/env bash
# Runs a command wrapped in btrfs snapper pre-post snapshots.
# Usage: $ snp <commands>
# e.g.: $ snp pacman -Syyu
# Requirements: snapper (https://wiki.archlinux.org/title/snapper)
# The latest version of this script is hosted at https://gist.github.com/erikw/5229436

log_path="/var/local/log/snp"
date=$(date "+%Y-%m-%d-%H%M%S")
log_file="${log_path}/snp_${date}.log"

jiffyclub

JamesMGreene

Initialize

gitflow	git
git flow init	git init
	git commit --allow-empty -m "Initial commit"
	git checkout -b develop master

Connect to the remote repository

EconometricsBySimulation

I recently found myself in need of a function to sample randomly from an arbitrarily defined probability density function. An excellent post by Quantitations shows how to accomplish this using some of Rs fairly sophisticated functional approximation tools such as integrate and uniroot. The only problem with this excellent post was that the machine cost was enormous with samples of 1000 draws taking 10 seconds on my machine and repeated samples of 100,000+ draws (which I was after) clearly being unworkable.

Thus I decided to take my own crack at it. First let us review the basics of drawing random variables from non-uniform distributions. The standard method I think most algorithms use works as follows:
  
  Assumptions
1. You can draw pseudo-random uniform variable u
2. You can integrate the pdf to construct a cdf
$$p = F(x) = \int_{-\infty}^\infty f(x) dx$$
  3. You can invert the cdf in order to solve for p
$$G(F(x))=F^{-1}(F(x))=F^{-1}(p)=x$$

aflaxman

prakhar1989

Rich Hickey on becoming a better developer

Rich Hickey • 3 years ago

Sorry, I have to disagree with the entire premise here.

A wide variety of experiences might lead to well-roundedness, but not to greatness, nor even goodness. By constantly switching from one thing to another you are always reaching above your comfort zone, yes, but doing so by resetting your skill and knowledge level to zero.

Mastery comes from a combination of at least several of the following:

ebressert

import seaborn as sns
from scipy.optimize import curve_fit

# Function for linear fit
def func(x, a, b):
    return a + b * x

# Seaborn conveniently provides the data for
# Anscombe's quartet.
df = sns.load_dataset("anscombe")

aflaxman

chrisrzhou

Git Advanced Resources

Interactive Rebase

• Enter interactive rebase using git rebase -i
• Some rebase options include:
  • squash: combine commits
  • edit: split commits (using git reset HEAD^)
  • reword: rename commit
  • pick: run commits in order

AustinRochford